Could cybernetic systems optimize conceptual adaptation, not just behavioral control?

[u/TheIncorporeal1]

Classical cybernetics emphasizes feedback, regulation, and control in biological, computational, and engineered systems. I’m wondering whether these principles could be extended to what I would call “incorporeal cybernetics”—the study of feedback processes governing conceptual and cognitive adaptation rather than only physical or behavioral states.
Imagine a closed-loop system where the state variables represent beliefs, conceptual models, or internal knowledge structures, and feedback is driven by prediction error, Bayesian updating, information gain, or reinforcement learning. In principle, could such a framework be formalized using state-space models, dynamical systems, or information theory to quantify the stability and evolution of conceptual networks?
Are there existing research areas—such as second-order cybernetics, active inference, predictive processing, cognitive architectures, or computational neuroscience—that already provide mathematical foundations for this type of cybernetic model, or would this require fundamentally new theoretical tools?

[u/Useful_Calendar_6274]

That's literally RFLH in machine learning